Creative designers in various fields work with materials from many different sources when they develop an advertisement, film, brochure, or other finished product. These materials may have been created originally by the creative designer, but for a different project, and stored in a media library or archive for later reuse. They may have been created by colleagues, and stored in an area that allows different individuals to collaborate on materials. Or they may have been created by professional artists for licensing or sale. These materials are often called rich media files, or assets. Rich media files may include, but are not limited to, video, photography, graphics, audio, mixed media files, logos, presentations, and text. These media files can exist in the widest possible range of formats. It is a ponderous task to manage such assets. Annotation mechanisms for such assets include the system described in U.S. Pat. No. 5,493,677, assigned to the same assignee as the assignee of the present invention. Systems to manage such assets include that described in U.S. Pat. No. 6,012,068.
It would be desirable to have a management system for digital media which streamlines the task of accounting for rights to use such media, including copyright rights. Systems relating to rights management include those described in U.S. Pat. Nos. 4,337,483, 5,201,047, 5,260,999, 5,263,158, 5,319,705, 5,438,508, 5,629,980, 5,765,152, and 5,553,143.
Users in different businesses may use different terminology to refer to the various media management functions. For example, some may use the term library, while others use archive. Some may use project workspace, while others use share or collaboration tool. In many systems, changing terminology requires tedious programming effort which risks introducing errors into the software. It would be desirable to have a management system that conveniently permits non-technical users to customize such terminology on a per-system basis. U.S. Pat. No. 5,850,561 describes a glossary construction tool for creating glossary from text.
Different companies using the software according to the invention may have different corporate culture, image, and system context within the company. It would be desirable to have a management system that conveniently permits non-technical users to customize the software on a per-system basis with respect to such corporate concerns.
In a typical asset management system, users browse through media file collections and view thumbnail images of files to decide which files they want to work with. These thumbnails are browseables, or small representations of the actual images, videos, or other media files in the system. A browseable is created by optimizing an image or video frame for online browsing, so a browseable has lower resolution and smaller dimensions than the original file. It is commonplace, however, to find that the resolution and dimensions are not well suited to the company. It would be desirable to have a management system that conveniently permits a system administrator to customize the software in this respect.
Natural language processing (NLP) techniques are well known, including their use in information retrieval applications (Strzalkowski, 1993), (Strzalkowski, Perez Carballo and Marinescu, 1995), (Evans and Zhai, 1996). Past systems have attempted to improve upon vocabulary management techniques, for example as described in U.S. Pat. Nos. 5,251,316 and 6,125,236. Past approaches for searching multimedia include U.S. Pat. Nos. 6,243,713 and 5,794,249.
Clustering is well known, for example in U.S. Pat. Nos. 5,317,507, 5,758,257, 5,675,819, 5,778,362, and 5,875,446. See also Buckley, Chris, J. Walz, M. Mitra and C. Cardie, “Using Clustering and Super Concepts within SMART: TREC 6” (http://trec.nist.gov/pubs/trec6/t6_proceedings.html); Zamir, Oren, O. Etzioni, Madani, and Karp, KDD “Fast And Intuitive Clustering Of Web Documents;” and Koller, Daphne, and Mehran Sahami, ML “Hierarchically Classifying Documents Using Very Few Words.” Rankings relating to relevance are discussed in U.S. Pat. No. 5,642,502.
The evaluation of information retrieval systems became an essential part of the field in the early '90s, and was strongly advanced by the TREC evaluations designed at NIST beginning in 1993. The TREC evaluation contains different tracks, but the tracks all share the following common features:                They are designed to provide a comparative evaluation between different systems, usually provided by different participants.        The evaluation is done using strict test conditions that contain a set of queries, a collection of documents, and relevance judgements.        The evaluations use evaluation scores such as precision and recall that supposedly predict real users' satisfaction from a system.        
While these evaluations are indeed helpful in comparing the performance of different IR systems, they do not provide constant feedback on the performance of a live IR system. The base performance of an IR system could be at first evaluated using a standard measurement such as the one above, but as more media files are added to a system and users submit queries in an uncontrolled manner, it is hard to predict or estimate the performance of the system. In addition, if the system does not fall into the initial TREC evaluation tracks, it is necessary to develop an independent test case —a very costly task. It is desirable to have a better self-evaluation system for such a digital asset manager.
Known annotation-related systems are discussed in U.S. Pat. Nos. 5,600,775, 6,006,241, and 5,938,724.